Quantifying and Screening Dynamic Phenotypes in Bacteria

Abstract

Many intracellular components display heterogeneous dynamics in space and time, with individual cells transiently deviating from average behavior. Flow cytometry and other snapshot-based methods not only fail to resolve dynamics, but when applied to pooled genetic libraries, the heterogeneity also makes it difficult to separate phenotypic outliers from genetically stable traits, even for the properties that are observed. To address this challenge, we took a continuous-culture microfluidic device that allows for multigenerational time-lapse imaging of cell lineages and equipped it with cell collection capabilities. Specifically, an optical trap for contactless cell manipulation, coupled with a network of flow channels and control valves for processing biofilms, enable reliable isolation of select cells without contamination and without causing mutations or influencing growth. The device, named SIFT for Single-cell Isolation Following Time-lapse microscopy, can track tens of thousands of bacterial lineages for hundreds of generations under precisely controlled local environments, enabling accurate screens of complex phenotypes. The method is compatible with undomesticated cell types and with any downstream molecular profiling as cells are physically accommodated and isolated, circumventing the need for barcoding or genetic modifications. SIFT was then used to identify more precise bacterial synthetic oscillators from pooled genetic libraries with mutants spanning a 30-fold range of average periods. This yielded the most coherent sub- and multi-generational oscillators known to date, with some variants oscillating for nearly 500 generations before drifting out of phase by just half a period. The results revealed novel design principles for precise timing in synthetic circuits and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes. The microfluidic growth platform was further leveraged to study single-cell protein degradation dynamics. Proteolysis in Escherichia coli mediated by the ssrA tag – one of the most prevalent degradation signals across natural and synthetic systems – was shown to follow Michaelis-Menten kinetics for the first time. Furthermore, the high affinity and rapid degradation of ssrA-tagged substrates were found to be dependent on SspB, an auxiliary degradation factor. Characterizing when proteases transition between unsaturated and saturated regimes is essential for understanding the dynamics of many protein networks.